Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation, offering a solution to the scarcity of data found in various scientific contexts. We demonstrate the proposed approach by applying it to an illustrative problem within the realm of near-field radiative heat transfer involving a multilayered hyperbolic metamaterial. We find that a successful generation of spectral data requires two modifications to conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid mode collapse, and, (ii) the conditioning of WGANs to obtain accurate labels for the generated data. We show that a simple feed-forward neural network (FFNN), when augmented with data generated by a CWGAN, enhances significantly its performance under conditions of limited data availability, demonstrating the intrinsic value of CWGAN data augmentation beyond simply providing larger datasets. In addition, we show that CWGANs can act as a surrogate model with improved performance in the low-data regime with respect to simple FFNNs. Overall, this work highlights the potential of generative machine learning algorithms in scientific applications beyond image generation and optimization.

翻译：生成对抗网络（GANs）是生成式人工智能领域中最稳健且最 versatile 的技术之一。本研究报道了GANs在合成光谱数据生成领域的应用，为科学背景下常见的数据稀缺问题提供了解决方案。我们通过近场辐射传热中涉及多层双曲超材料的示例问题，验证了所提出的方法。研究发现，成功生成光谱数据需要对传统GANs进行两项改进：（i）引入Wasserstein生成对抗网络（WGANs）以避免模式崩溃；（ii）对WGANs进行条件化处理，为生成数据获取准确标签。我们证明，当简单前馈神经网络（FFNN）使用条件WGAN（CWGAN）生成的数据进行增强时，在数据有限条件下其性能显著提升，这体现了CWGAN数据增强超越单纯扩大数据集的固有价值。此外，我们表明在低数据场景下，CWGAN作为替代模型的性能优于简单FFNN。总体而言，本研究突出了生成式机器学习算法在图像生成与优化之外的科学研究中的应用潜力。